Sure Pass Exam MLS-C01 PDF

The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1

The other options are incorrect because they either do not update the software or require unnecessary steps. For example:

• Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2
• Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3
• Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4

References:

• 1: Amazon SageMaker Notebook Instances - Amazon SageMaker
• 2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker
• 3: Create a Notebook Instance - Amazon SageMaker
• 4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker

Image classification is a machine learning task that involves assigning labels to images based on their content. Image classification can be performed using various algorithms, such as convolutional neural networks (CNNs), which are a type of deep learning model that can learn to extract high-level features from images. To train a customized image classification model, the e-commerce company needs a compute choice that can support the high computational demands of deep learning and provide good accuracy on the model quickly. A GPU accelerated computing instance, such as p3.2xlarge, is a suitable choice for this task, as it can leverage the parallel processing power of GPUs to speed up the training process and reduce the training time. A p3.2xlarge instance has one NVIDIA Tesla V100 GPU, which can provide up to 125 teraflops of mixed-precision performance and 16 GB of GPU memory. A p3.2xlarge instance can also use various deep learning frameworks, such as TensorFlow, PyTorch, MXNet, etc., to build and train the image classification model. A p3.2xlarge instance is also more cost-effective than a p3.8xlarge instance, which has four NVIDIA Tesla V100 GPUs, as the latter may not be necessary for a proof of concept with a small dataset. Therefore, the Machine Learning Specialist should select p3.2xlarge as the compute choice to train and achieve good accuracy on the model quickly.

References:

• Amazon EC2 P3 Instances - Amazon Web Services
• Image Classification - Amazon SageMaker
• Convolutional Neural Networks - Amazon SageMaker
• Deep Learning AMIs - Amazon Web Services

 Residual plots are a model evaluation technique that can be used to understand whether a regression model is more frequently overestimating or underestimating the target. Residual plots are graphs that plot the residuals (the difference between the actual and predicted values) against the predicted values or other variables. Residual plots can help the Machine Learning Specialist to identify the patterns and trends in the residuals, such as the direction, shape, and distribution. Residual plots can also reveal the presence of outliers, heteroscedasticity, non-linearity, or other problems in the model12

To determine whether the model is overestimating or underestimating the target, the Machine Learning Specialist can use a residual plot that plots the residuals against the predicted values. This type of residual plot is also known as a prediction error plot. A prediction error plot can show the magnitude and direction of the errors made by the model. If the model is overestimating the target, the residuals will be negative, and the points will be below the zero line. If the model is underestimating the target, the residuals will be positive, and the points will be above the zero line. If the model is accurate, the residuals will be close to zero, and the points will be scattered around the zero line. A prediction error plot can also show the variance and bias of the model. If the model has high variance, the residuals will have a large spread, and the points will be far from the zero line. If the model has high bias, the residuals will have a systematic pattern, such as a curve or a slope, and the points will not be randomly distributed around the zero line. A prediction error plot can help the Machine Learning Specialist to optimize the model by adjusting the complexity, features, or parameters of the model34

The other options are not valid or suitable for determining whether the model is overestimating or underestimating the target. Root Mean Square Error (RMSE) is a model evaluation metric that measures the average magnitude of the errors made by the model. RMSE is the square root of the mean of the squared residuals. RMSE can indicate the overall accuracy and performance of the model, but it cannot show the direction or distribution of the errors. RMSE can also be influenced by outliers or extreme values, and it may not be comparable across different models or datasets5 Area under the curve (AUC) is a model evaluation metric that measures the ability of the model to distinguish between the positive and negative classes. AUC is the area under the receiver operating characteristic (ROC) curve, which plots the true positive rate against the false positive rate for various classification thresholds. AUC can indicate the overall quality and performance of the model, but it is only applicable for binary classification models, not regression models. AUC cannot show the magnitude or direction of the errors made by the model. Confusion matrix is a model evaluation technique that summarizes the number of correct and incorrect predictions made by the model for each class. A confusion matrix is a table that shows the counts of true positives, false positives, true negatives, and false negatives for each class. A confusion matrix can indicate the accuracy, precision, recall, and F1-score of the model for each class, but it is only applicable for classification models, not regression models. A confusion matrix cannot show the magnitude or direction of the errors made by the model.

To control data egress from SageMaker, the ML engineer can use the following mechanisms:

• Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. This allows the ML engineer to access SageMaker services and resources without exposing the traffic to the public internet. This reduces the risk of data leakage and unauthorized access1
• Enable network isolation for training jobs and models. This prevents the training jobs and models from accessing the internet or other AWS services. This ensures that the data used for training and inference is not exposed to external sources2
• Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys. This enables the ML engineer to encrypt the data stored in Amazon S3 buckets, SageMaker notebook instances, and SageMaker endpoints. It also allows the ML engineer to encrypt the data in transit between SageMaker and other AWS services. This helps protect the data from unauthorized access and tampering3

The other options are not effective in controlling data egress from SageMaker:

• Use SCPs to restrict access to SageMaker. SCPs are used to define the maximum permissions for an organization or organizational unit (OU) in AWS Organizations. They do not control the data egress from SageMaker, but rather the access to SageMaker itself4
• Disable root access on the SageMaker notebook instances. This prevents the users from installing additional packages or libraries on the notebook instances. It does not prevent the data from being transferred out of the notebook instances.
• Restrict notebook presigned URLs to specific IPs used by the company. This limits the access to the notebook instances from certain IP addresses. It does not prevent the data from being transferred out of the notebook instances.

References:

• 1: Amazon SageMaker Interface VPC Endpoints (AWS PrivateLink) - Amazon SageMaker
• 2: Network Isolation - Amazon SageMaker
• 3: Encrypt Data at Rest and in Transit - Amazon SageMaker
• 4: Using Service Control Policies - AWS Organizations
• : Disable Root Access - Amazon SageMaker
• : Create a Presigned Notebook Instance URL - Amazon SageMaker